The Northwest Atlantic ocean bordering the United states and Nova Scotia is one of the fastest warming locations on Earth. Research on the impacts of this rapid-warming has primarily focused on high-profile and/or upper trophic level species. Here we investigate an the ecosystem wide impacts by integrating community size structure changes using size spectrum analysis. While laboratory studies and ecological theory suggest that ectotherms raised at higher temperatures will reach smaller body-sizes at comparable development stages, it is unclear whether that relationship can be mitigated against through the adaptive behaviors of diverse communities. In cases where community responses fail at adapting to elevated temperatures, we anticipate a steepening of the body-size spectrum slope relating to a reduction in larger sized individuals and an increase in smaller sized individuals within that community. Using data from fisheries independent surveys we estimated size spectrum indices for four regions along the US NE continental shelf. At this regional scale, we found that declines in the size spectrum slopes occurred the strongest in the Northernmost regions of the Gulf of Maine and Georges Bank. These areas were historically home to the coldest temperatures and the largest populations of commercially targeted groundfish species. Size spectrum slope declines were more pronounced in the 80’s and 90’s, before to the more-recent elevated temperatures. This suggests that other external forces likely drove the initial declines of larger-sized individuals within the communities. While the primary pressure of fisheries exploitation has declined over time, the recovery of larger-sized individuals has not been seen and remains threatened by elevated temperatures.
Introduction
Temperature & Ecology
It is well understood that temperature plays a critical role on physiology through its impact on the chemical reactions that underpin biological life. A consequence of this, is that most life has evolved a thermal preference, around which the chemical reactions that support things like growth and means for self-preservation. Species that are unable to maintain their optimal thermal preferences internally (ectotherms), must be able to follow their thermal preferences through locomotion or adapt through changes in behavior. Otherwise; there will be physiological and/or metabolic costs in failing to do so.
In an era of anthropogenic climate change, there is an expectation that many species will be displaced from historic habitats. Research in marine environments has shown examples of species shifting to higher latitudes and deeper depths in the pursuit of more favorable conditions (Kleisner et al. 2017; Pinsky et al. 2013). Other research has suggested that the impacts of elevated temperatures may be manifesting not through geographic distribution change, but through physiological changes, changes in seasonal phenology, or hampered recovery efforts (Daan et al. 2005; Miller, O’Brien, and Fratantoni 2018; A. Pershing et al. 2018; University of South Carolina et al. 2021). Temperature has direct and indirect impacts on critical biological functions including the acquisition of biomass through feeding, the rates of biomass loss through metabolism, and the rates at which individuals mature and develop. This potential for temperature to impact the size structure of an ecosystem has implications for blue economy & natural resource systems we rely upon.
Size Spectrum Theory
Size is a defining characteristic of species that mediates many ecological interactions (Brown, West, and Enquist 2000). Size impacts the mobility of an organism, its ability to evade predation, its ability to successfully prey on other organisms, and the metabolic costs associated with each of these behaviors. In the context of a strongly size-structured ecosystem, growth and maturity changes alter fitness and ultimately determine whether a species is successful in the given environment. Size structured environments are a fundamental organizational pattern that has been heavily researched. Ecological theory is rich with models relating how energy transfers from smaller prey species to larger predatory trophic levels, the allocation of energy for growth, and the trade offs of allocating energy towards reproduction at the expense of growth. One such ecological model that avoids the need for explicit articulation of each predator-prey interaction is the size spectrum model.
A “size spectrum” describes the distribution of biomass or abundance as a function of individuals’ mass or size on a log–log scale (Guiet, Poggiale, and Maury 2016). Size spectrum are described by two terms, the size spectrum slope & intercept. These two terms convey the baseline productivity, and how energy flows through an ecosystem in the form of biomass. The spectrum intercept value captures the richness or the productivity at the base of the community and is strongly connected to the prevailing environmental conditions (Boudreau and Dickie 1992). So much so, that spectrum shape is sometimes defined by its eutrophic or oligotrophic environmental conditions (Rossberg 2012).
Size spectra condense the complexities of predator prey networks and their interactions into a handful of size-based indices (SBI). In doing so a community size spectrum captures the emergent properties of a system, while needing only size and abundance information that is commonly available. For this reason it has become increasingly relied upon as an indicator of ecosystem health in the push for ecosystem based fisheries management. Changes in slope have been associated with fishing exploitation, primarily through the targeted removal of larger individuals (Bianchi et al. 2000; Shin et al. 2005). Numerical experiments have also tried to link changes in slope to environmental disturbances (Guiet, Poggiale, and Maury 2016). Biomass spectrum present predictable intercepts between ecosystems of similar productivity levels, but also of distinct temperatures (Guiet, Poggiale, and Maury 2016).
Because it controls chemical reactions, temperature controls metabolic rates which underpin maintenance, growth or reproduction (Clarke and Johnston, 1999; Kooijman, 2010) as well as the functional responses to food density (Rall et al., 2012). Guiet et al. (2016)… In addition to the impact of temperature on communities’ intercepts (heights), the impact of temperature on the speed of the energy flow within communities may affect other properties, such as their resilience to perturbations or the intensity of trophic cascades (Andersen and Pedersen, 2009).
Sea surface temperatures in the Gulf of Maine since 1982 have been warming at rates faster than 96% of the world’s oceans, with similar warming rates along the northwest Atlantic shelf (A. Pershing et al. 2018). A punctuated elevation in temperatures over the last decade are believed to be the result of a shift in Gulf Stream positioning. A Northward shift in the Gulf stream has increased the regional temperatures via an increased direct transport of warm gulf stream water into areas like the Gulf of Maine . The Gulf stream has also been producing a higher frequency of warm core rings, and has obstructed some of the cold-water Scotian Shelf current flow that would otherwise counter the influence of the Gulf Stream on the region’s temperatures (Gangopadhyay et al. 2019; University of South Carolina et al. 2021). The combination of these oceanographic changes has led to a warmer continental shelf habitat.
Species Trends in NE Shelf
The continental shelf groundfish community in Northwest Atlantic has changed dramatically over the last century. Stocks that supported international fishing effort collapsed, and recovery efforts fell short of their objectives. Research on Georges Bank estimated that biomass more than halved in the 1960’s (pre-dating federal monitoring efforts), and noted a species replacement of commercial groundfish target species by skate and dogfish (Fogarty and Murawski 1998). Fishing is inherently size-selective, with larger individuals selectively removed from the population. This has an immediate impact on the community size-distribution with additional impacts on the future population as well. Larger individuals have a greater impact on population recovery, capable of holding more (and often of higher quality) eggs. Size-based harvest in fisheries has been shown to create selective pressures that promote characteristics of early maturation at smaller sizes.
In addition to the ecological disturbance of fisheries removals, this region is also one of the fastest warming locations in the global oceans. The rapid warming in the northwest Atlantic is a major factor in the redistribution of marine species along the US east coast. Species have responded by adjusting the timing and locations of their seasonal migrations and shifting their geographic ranges (Nye et al. 2009; Staudinger et al. 2019). There is evidence that warming has hampered fisheries recoveries as well. Adding a metabolic tax to physiological pathways like growth and metabolism. Species like Black Sea Bass, Atlantic shortfin squid, and Blue crab have been high-profile examples of species expanding their ranges to follow their thermal preferences. While species like the American lobster have shown declines at their southern range near Long Island Sound, with much doubt whether they will recover under the present temperature trends. The recent regime shift in the physical oceanography has also shown to be a catalyst for biological shifts as well (University of South Carolina et al. 2021; Perretti et al. 2017).
While these examples show that species can respond to changes in the physical environment around them through movement & behavior, research elsewhere suggests that physiological responses integrated across species will manifest as changes in community size structure.
Purpose
With the understanding that populations depend on the health of their ecosystems, there is a need to have community-wide metrics to effectively understand and manage marine resources. Size based indices are metrics that can be estimated from the information that has historically been available from long-term survey efforts. These indices have been shown to be sensitive to the impacts of fishing, but should also capture environmentally driven stress as well. We estimated size spectrum relationships as SBI’s for the groundfish populations for each sub-region of the Northeast US continental shelf. In the case of the NW Atlantic sustained increases in temperature should have a physiological impact on the community size structure.
This leads to our second hypothesis:
H2. Warming alters the community through the direct influence of temperature on metabolism, growth, and population productivity.
Methods
Groundfish Data
Fishery Independent data on was collected as part of the NEFSC’s northeast trawl survey. This survey is conducted each year in the spring and in the fall, with sample locations determined following a stratified-random survey design with effort allocated in proportion to stratum area. Analyses using the trawl survey data took used data from both the Spring and Fall survey seasons covering all years from 1970 to 2019. Correction factors were applied to total species abundance and biomass to account for changes in vessels, gear, and doors when appropriate as part of the survey program. However, abundance and biomass by length is not corrected. To account for this, abundance at length for each species were adjusted to match the correction factors applied to total species abundance at each station, with allocation following the distributions of length caught at that station. Such that for each species: the sum of adjusted abundances for each length class is equal to the gross abundance as corrected for changes in vessels, gear, etc. To account for differences in sampling effort among survey strata, all corrected abundance-at-length data was area-stratified.
Data from the survey was grouped by strata to form geographically meaningful sub-regions: Gulf of Maine, Georges Bank, Southern New England, Mid-Atlantic Bight. For each region, we developed several time series indicators:
Community Composition metrics (abundance and biomass by functional group, with body-size contributions)
Mean size of the aggregate community and key functional groups
Slope and intercept of the size spectrum
Community Composition
Functional groups were assigned to each species based on life history and geography. Functional groups included were elasmobranch, pelagic, demersal, and groundfish species. Stratified annual abundance and biomass totals were calculated for each functional group and each region with labels for increasing body-size (biomass kg) groups.
Body Size Trends
The annual stratified-abundance weighted body length and body weight within each region and for each functional group were also estimated using the numbers at length and the estimated biomass at length information. Data for body size trends was not truncated at 1g minimum body weight.
Size Spectrum Analysis
Community size spectra were estimated using abundance-at-length data from 68 species. These species were selected based on the availability of published weight-at-length relationships (Wigley et al. 2003) and represented 98.98% of the total biomass caught in the survey. Published length-weight relationships were used to convert abundance at length data into their corresponding biomass at length (kg). These values were then used to get totals for stratified weight-at-length, in complement to the corrected abundances-at-length data which had been area stratified. These area-stratified biomass at length totals were then used for fitting each regional biomass size spectra.
To fit the normalized biomass size spectra, stratified biomass at length data was binned into logartithmically equal spaced intervals (0.5 on a \(log_{10}\) scale), summing bodymass across all species within each body size bin. To normalize the spectra, the atratified abundances within each bin was then divided by the bin-width to account for the increasing bin-widths, a consequence of the log scale. Normalized size spectra were fit for each year and for each region independently, and for each year across all strata, using ordinary least squares (ols) regressions for stratified abundance (normalized) by body-size bins.
Temperature Data
Global Sea surface temperature data was obtained via NOAA’s optimally interpolated SST analysis (OISSTv2), providing daily temperature values at a 0.25° latitude x 0.25° longitude resolution (Reynolds et al. 2007). A daily climatology for every 0.25° pixel in the global data set was created using average daily temperatures spanning the period of 1982-2011. Daily anomalies were then computed as the difference between observed temperatures and the daily climatological average. OISSTv2 data used in these analyses were provided by the NOAA PSL, Boulder, Colorado, USA from their website at https://psl.noaa.gov.
Temperature data was regionally averaged to match the survey regions from the age-at-length data. SST anomalies were averaged by year for each region and over the entire sampling region to produce daily time series. These time series were then processed into annual timeseries of surface temperatures and anomalies. All region-averaging was done with area-weighting of the latitude/longitude grid cells to account for differences in cell-size in of the OISSTv2 data.
Spectra Drivers
The impact of external factors on the changes in size spectra was correlated against several hypothesized driving forces related to both environmental regimes and anthropogenic disturbances. Potential environmental drivers include sea surface temperature anomalies, Gulf Stream Index (GSI), and zooplankton community indices from the continuous plankton recorder (CPR) dataset. Anthropogenic drivers include state and federal fisheries landings from the Greater Atlantic Regional Fisheries Office (GARFO), divided by reporting zones into aggregate regions to closely align with the survey areas we defined for the size spectra analyses.
{fig-align=“center”,width = “420”}
Results
Abundance Distribution
Abundance across all body sizes remained relatively stable from the 1970’s before rising in the northern regions around 1990 beginning in the Gulf of Maine. Around this time abundances increased through the mid 2010’s. Further south in Georges Bank, abundances remained flat until the 2010’s, when overall abundance roughly tripled, only to fall back to previous amounts by the end of the century. Southern New England saw a less dramatic rise and fall that began just before 2010, again falling back to earlier levels by the end of the century. The Mid Atlantic Bight had relatively consistent abundances throughout, with no major periods of abundance growth or decline, but with larger inter-annual variability.
Abundance gains observed in Georges Bank and Gulf of Maine were primarily from groundfish species, with additional growth from demersal species in the Gulf of Maine. Increases in abundance across all areas was mostly confined to individuals weighing less than .5kg. With some years driven in large-part by exceptional year-classes in a handful of species like haddock in Georges Bank. The observed abundance volatility in Southern New England and the Mid-Atlantic Bight conversely was largely the result of changes in abundance in pelagic species, whose abundance varied by several times the magnitude that of the other functional groups.
Biomass Distribution
Overall biomass was highest in the two northern regions, the Gulf of Maine and Georges Bank. Roughly half of the biomass sampled in these regions can be attributed to groundfish/demersal species, with the second largest contributions coming from elasmobranchs. Groundfish biomass, larger individuals >2kg in particular, declined during the 70’s and 80’s in these regions, never truly recovering. Beginning in the 2000’s there were signs that groundfish abundances were increasing as evidenced by increasing numbers of smaller individuals, however in both regions this trend appears to have reversed by the mid 2010’s. Elasmobranch biomass increased steadily throughout the survey time period across all regions, with the exception of southern New England. This area showed large 5-10 year swings in biomass, but no clear long-term trend. Larger elasmobranch were rare in all regions except for a period spanning the late 70’s through the early 90’s isolated to Georges Bank. Demersal species biomass was highest in the Gulf of Maine, dwarfing their contributions in other regions. Their biomass declined in the 70’s, was flat until the late 90’s, remaining relatively high until declining in the late 2010’s. Pelagic species biomass was low in all regions, and is unlikely to be representative of true biomass trends due to gear selectivity.
Regional Variation in Species Composition
There was a distinct difference between Northern and Southern regions in the way biomass was distributed among the different functional groups. The primary contributors to overall biomass in the southern regions (southern New England & mid-Atlantic bight) was the elasmobranch community. While the northern regions (Gulf of Maine & Georges Bank) each had similar quantities of elasmobranch biomasses, there was also a comparable contribution of groundfish and in the Gulf of Maine there was a major component of demersal species as well.
Body Size Trends
The average fish size in the Gulf of Maine (length and weight) declined the greatest of all regions over our study period. The average individual length was greatest in the 1970’s in the 35-40cm range, falling to 28-33cm over the last decade. Body-weight fell dramatically in the 1980’s, from around .75kg in the 1970’s to .25-.30kg, roughly a third of what it had been. Georges Bank body sizes also declined during the study period, but less dramatically. Both of these Northern regions had brief period in the late 2000’s where average length and weight rose, before falling again in the 2010’s. The MAB region was the only region to see a long-term increase in both length and weight during the study period. SNE saw no long-term change in length, and a minor decline in average body-weight.
Regional Size Spectra
Size spectrum slopes were least steep in the two Northern regions.
The data we rely on in this analysis was collected as part of a survey program which began out of concern that fisheries were already being over-harvested. Estimates by scientists at that time suggested that by the 1970’s total biomass of Georges Bank had been halved and elasmobranchs had begun to replace the over-exploited gadoids (Fogarty and Murawski 1998). The implication of such a large disturbance that pre-dates our time series is that the steepening of size spectrum slope we observed in this area and the adjacent Gulf of Maine are the tail-ends of a longer and more severe decline. While metrics of overall fishing pressure is hard to align exactly with trawl survey coverage, historic and anecdotal evidence show that groundfish fishing pressures are a fraction of their historic pressure was in the 1960’s and 1970’s. This begs the question of why larger adult numbers never began to recover in these regions. Looking at abundance and biomass information from the survey there was evidence of strong recruitment among smaller individuals < 1kg, but there has since not been any sizable population of fishes larger than 1kg outside of elasmobranchs. Work by (A. J. Pershing et al. 2015) suggests that part of the failure in recovery was due to an inability to account for temperature change in fisheries management. At this point in time the regional temperatures had just begun to reflect a regime shift, and could have been considered at that time an acute stressor. Since then the region has experienced nearly a decade of sustained above-average temperatures, and there are signs that the success seen in recruitment and survival of even the smaller size classes is declining.
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Source Code
---title: "Raising Temperatures Extend Fisheries-Exploitation Harm to the Community Size Structure of Northeast US Groundfishes"author: name: "Adam Kemberling" url: https://github.com/adamkemberling affiliation: Gulf of Maine Research Institutedescription: | Size Spectrum Analysis of the Northeast US Groundfish Communitydate: "`r Sys.Date()`"# format: docxformat: html: self-contained: true code-fold: true code-tools: true df-print: kable toc: true toc-depth: 2editor: sourceexecute: echo: false warning: false message: false fig.height: 6 fig.width: 6 fig.align: "center" comment: ""bibliography: references.bib---```{r}#| label: load packages and functions#### Packages ####library(targets)library(rnaturalearth)library(here)library(sf)library(gmRi)library(patchwork)library(gt)library(knitr)library(tidyverse)library(ggstream)library(ggforce)library(bcp)library(scales)library(corrplot)library(readxl)# Support functionssource(here("R/support/sizeSpectra_support.R"))# Set themetheme_set(theme_gmri())# Map polygonsus_poly <-ne_states("united states of america", returnclass ="sf")canada <-ne_states("canada", returnclass ="sf")# Function to process summaries for various factor combinationsget_group_summaries <-function(...){# Do some grouping to get totals group_totals <- nefsc_size_bins %>%group_by(...) %>%summarise(total_survey_catch =sum(numlen, na.rm = T),total_lw_bio =sum(sum_weight_kg, na.rm = T),total_strat_abund =sum(strat_total_abund_s, na.rm = T),total_strat_lw_bio =sum(strat_total_lwbio_s, na.rm = T), .groups ="drop") # length bins group_lengths <- nefsc_size_bins %>%group_by(..., length_bin) %>%summarise(lenbin_survey_catch =sum(numlen),lenbin_lw_bio =sum(sum_weight_kg),lenbin_strat_abund =sum(strat_total_abund_s),lenbin_strat_lw_bio =sum(strat_total_lwbio_s), .groups ="drop") %>%left_join(group_totals) %>%mutate(perc_total_catch = (lenbin_survey_catch - total_survey_catch) *100,perc_lw_bio = (lenbin_lw_bio - total_lw_bio) *100,perc_strat_abund = (lenbin_strat_abund - total_strat_abund) *100,perc_strat_lw_bio = (lenbin_strat_lw_bio - total_strat_lw_bio) *100)# weight bins group_weights <- nefsc_size_bins %>%group_by(..., weight_bin) %>%summarise(wtbin_survey_catch =sum(numlen),wtbin_lw_bio =sum(sum_weight_kg),wtbin_strat_abund =sum(strat_total_abund_s),wtbin_strat_lw_bio =sum(strat_total_lwbio_s),.groups ="drop") %>%left_join(group_totals) %>%mutate(perc_total_catch = (wtbin_survey_catch / total_survey_catch) *100,perc_lw_bio = (wtbin_lw_bio / total_lw_bio) *100,perc_strat_abund = (wtbin_strat_abund / total_strat_abund) *100,perc_strat_lw_bio = (wtbin_strat_lw_bio / total_strat_lw_bio) *100)return(list("length_bins"=drop_na(group_lengths),"weight_bins"=drop_na(group_weights)))}```# AbstractThe Northwest Atlantic ocean bordering the United states and Nova Scotia is one of the fastest warming locations on Earth. Research on the impacts of this rapid-warming has primarily focused on high-profile and/or upper trophic level species. Here we investigate an the ecosystem wide impacts by integrating community size structure changes using size spectrum analysis. While laboratory studies and ecological theory suggest that ectotherms raised at higher temperatures will reach smaller body-sizes at comparable development stages, it is unclear whether that relationship can be mitigated against through the adaptive behaviors of diverse communities. In cases where community responses fail at adapting to elevated temperatures, we anticipate a steepening of the body-size spectrum slope relating to a reduction in larger sized individuals and an increase in smaller sized individuals within that community. Using data from fisheries independent surveys we estimated size spectrum indices for four regions along the US NE continental shelf. At this regional scale, we found that declines in the size spectrum slopes occurred the strongest in the Northernmost regions of the Gulf of Maine and Georges Bank. These areas were historically home to the coldest temperatures and the largest populations of commercially targeted groundfish species. Size spectrum slope declines were more pronounced in the 80's and 90's, before to the more-recent elevated temperatures. This suggests that other external forces likely drove the initial declines of larger-sized individuals within the communities. While the primary pressure of fisheries exploitation has declined over time, the recovery of larger-sized individuals has not been seen and remains threatened by elevated temperatures.# Introduction## Temperature & EcologyIt is well understood that temperature plays a critical role on physiology through its impact on the chemical reactions that underpin biological life. A consequence of this, is that most life has evolved a thermal preference, around which the chemical reactions that support things like growth and means for self-preservation. Species that are unable to maintain their optimal thermal preferences internally (ectotherms), must be able to follow their thermal preferences through locomotion or adapt through changes in behavior. Otherwise; there will be physiological and/or metabolic costs in failing to do so.In an era of anthropogenic climate change, there is an expectation that many species will be displaced from historic habitats. Research in marine environments has shown examples of species shifting to higher latitudes and deeper depths in the pursuit of more favorable conditions [@kleisner2017; @pinsky2013]. Other research has suggested that the impacts of elevated temperatures may be manifesting not through geographic distribution change, but through physiological changes, changes in seasonal phenology, or hampered recovery efforts [@daan2005; @miller2018; @pershing2018; @meyer-gutbrod2021]. Temperature has direct and indirect impacts on critical biological functions including the acquisition of biomass through feeding, the rates of biomass loss through metabolism, and the rates at which individuals mature and develop. This potential for temperature to impact the size structure of an ecosystem has implications for blue economy & natural resource systems we rely upon.## Size Spectrum TheorySize is a defining characteristic of species that mediates many ecological interactions [@brown2000]. Size impacts the mobility of an organism, its ability to evade predation, its ability to successfully prey on other organisms, and the metabolic costs associated with each of these behaviors. In the context of a strongly size-structured ecosystem, growth and maturity changes alter fitness and ultimately determine whether a species is successful in the given environment. Size structured environments are a fundamental organizational pattern that has been heavily researched. Ecological theory is rich with models relating how energy transfers from smaller prey species to larger predatory trophic levels, the allocation of energy for growth, and the trade offs of allocating energy towards reproduction at the expense of growth. One such ecological model that avoids the need for explicit articulation of each predator-prey interaction is the size spectrum model.A "size spectrum" describes the distribution of biomass or abundance as a function of individuals' mass or size on a log--log scale [@guiet2016]. Size spectrum are described by two terms, the size spectrum slope & intercept. These two terms convey the baseline productivity, and how energy flows through an ecosystem in the form of biomass. The spectrum intercept value captures the richness or the productivity at the base of the community and is strongly connected to the prevailing environmental conditions [@boudreau1992]. So much so, that spectrum shape is sometimes defined by its eutrophic or oligotrophic environmental conditions [@rossberg2012].Size spectra condense the complexities of predator prey networks and their interactions into a handful of size-based indices (SBI). In doing so a community size spectrum captures the emergent properties of a system, while needing only size and abundance information that is commonly available. For this reason it has become increasingly relied upon as an indicator of ecosystem health in the push for ecosystem based fisheries management. Changes in slope have been associated with fishing exploitation, primarily through the targeted removal of larger individuals [@bianchi2000; @shin2005]. Numerical experiments have also tried to link changes in slope to environmental disturbances [@guiet2016]. Biomass spectrum present predictable intercepts between ecosystems of similar productivity levels, but also of distinct temperatures [@guiet2016].This is a direct quote from [@guiet2016], but nails the connection back to temp expectations:> Because it controls chemical reactions, temperature controls metabolic rates which underpin maintenance, growth or reproduction (Clarke and Johnston, 1999; Kooijman, 2010) as well as the functional responses to food density (Rall et al., 2012). Guiet et al. (2016)... In addition to the impact of temperature on communities' intercepts (heights), the impact of temperature on the speed of the energy flow within communities may affect other properties, such as their resilience to perturbations or the intensity of trophic cascades (Andersen and Pedersen, 2009).### Temperature of the Gulf of Maine & NE Shelf- Marine species distribution shifts in NE Atlantic shelf [@kleisner2017]Sea surface temperatures in the Gulf of Maine since 1982 have been warming at rates faster than 96% of the world's oceans, with similar warming rates along the northwest Atlantic shelf [@pershing2018]. A punctuated elevation in temperatures over the last decade are believed to be the result of a shift in Gulf Stream positioning. A Northward shift in the Gulf stream has increased the regional temperatures via an increased direct transport of warm gulf stream water into areas like the Gulf of Maine . The Gulf stream has also been producing a higher frequency of warm core rings, and has obstructed some of the cold-water Scotian Shelf current flow that would otherwise counter the influence of the Gulf Stream on the region's temperatures [@gangopadhyay2019; @meyer-gutbrod2021]. The combination of these oceanographic changes has led to a warmer continental shelf habitat.### Species Trends in NE ShelfThe continental shelf groundfish community in Northwest Atlantic has changed dramatically over the last century. Stocks that supported international fishing effort collapsed, and recovery efforts fell short of their objectives. Research on Georges Bank estimated that biomass more than halved in the 1960's (pre-dating federal monitoring efforts), and noted a species replacement of commercial groundfish target species by skate and dogfish [@fogarty1998]. Fishing is inherently size-selective, with larger individuals selectively removed from the population. This has an immediate impact on the community size-distribution with additional impacts on the future population as well. Larger individuals have a greater impact on population recovery, capable of holding more (and often of higher quality) eggs. Size-based harvest in fisheries has been shown to create selective pressures that promote characteristics of early maturation at smaller sizes.In addition to the ecological disturbance of fisheries removals, this region is also one of the fastest warming locations in the global oceans. The rapid warming in the northwest Atlantic is a major factor in the redistribution of marine species along the US east coast. Species have responded by adjusting the timing and locations of their seasonal migrations and shifting their geographic ranges [@nye2009; @staudinger2019]. There is evidence that warming has hampered fisheries recoveries as well. Adding a metabolic tax to physiological pathways like growth and metabolism. Species like Black Sea Bass, Atlantic shortfin squid, and Blue crab have been high-profile examples of species expanding their ranges to follow their thermal preferences. While species like the American lobster have shown declines at their southern range near Long Island Sound, with much doubt whether they will recover under the present temperature trends. The recent regime shift in the physical oceanography has also shown to be a catalyst for biological shifts as well [@meyer-gutbrod2021; @perretti2017].While these examples show that species can respond to changes in the physical environment around them through movement & behavior, research elsewhere suggests that physiological responses integrated across species will manifest as changes in community size structure.### PurposeWith the understanding that populations depend on the health of their ecosystems, there is a need to have community-wide metrics to effectively understand and manage marine resources. Size based indices are metrics that can be estimated from the information that has historically been available from long-term survey efforts. These indices have been shown to be sensitive to the impacts of fishing, but should also capture environmentally driven stress as well. We estimated size spectrum relationships as SBI's for the groundfish populations for each sub-region of the Northeast US continental shelf. In the case of the NW Atlantic sustained increases in temperature should have a physiological impact on the community size structure.This leads to our second hypothesis:> #### H2. Warming alters the community through the direct influence of temperature on metabolism, growth, and population productivity.# Methods## Groundfish Data```{r}#| label: load survdat_data# 1. Biological data used as inputwithr::with_dir(rprojroot::find_root('_targets.R'), tar_load(nefsc_1g_labelled)) # rename and formatnefsc_size_bins <- nefsc_1g_labelledn_species <-length(unique(nefsc_size_bins$comname))```Fishery Independent data on was collected as part of the NEFSC's northeast trawl survey. This survey is conducted each year in the spring and in the fall, with sample locations determined following a stratified-random survey design with effort allocated in proportion to stratum area. Analyses using the trawl survey data took used data from both the Spring and Fall survey seasons covering all years from 1970 to 2019. Correction factors were applied to total species abundance and biomass to account for changes in vessels, gear, and doors when appropriate as part of the survey program. However, abundance and biomass by length is not corrected. To account for this, abundance at length for each species were adjusted to match the correction factors applied to total species abundance at each station, with allocation following the distributions of length caught at that station. Such that for each species: the sum of adjusted abundances for each length class is equal to the gross abundance as corrected for changes in vessels, gear, etc. To account for differences in sampling effort among survey strata, all corrected abundance-at-length data was area-stratified.```{r}#| label: make region map# Get region polygons:# Get the paths to the shapefiles used to masktrawl_paths <- gmRi::get_timeseries_paths(region_group ="nmfs_trawl_regions", box_location ="cloudstorage")# Polygons for each regionall_poly <-read_sf(trawl_paths[["inuse_strata"]][["shape_path"]]) ggplot() +geom_sf(data = us_poly) +geom_sf(data = canada) +geom_sf(data = all_poly, aes(fill = survey_area), alpha =0.8) +coord_sf(xlim =c(-76.4, -64.4), ylim =c(35, 45.5), expand = F) +scale_fill_gmri() +theme_bw() +map_theme(legend.position ="bottom", legend.background =element_rect(fill ="white")) +guides(fill =guide_legend(title ="", nrow =2))```Data from the survey was grouped by strata to form geographically meaningful sub-regions: Gulf of Maine, Georges Bank, Southern New England, Mid-Atlantic Bight. For each region, we developed several time series indicators:1. Community Composition metrics (abundance and biomass by functional group, with body-size contributions)2. Mean size of the aggregate community and key functional groups3. Slope and intercept of the size spectrum### Community CompositionFunctional groups were assigned to each species based on life history and geography. Functional groups included were elasmobranch, pelagic, demersal, and groundfish species. Stratified annual abundance and biomass totals were calculated for each functional group and each region with labels for increasing body-size (biomass kg) groups.```{r}#| label: group biomass and abundance data# make a column that contains all datanefsc_size_bins <- nefsc_size_bins %>%mutate(group_col ="All Data") # Do some grouping by year survey area and functional groupfgroup_area <-get_group_summaries(Year, survey_area, spec_class)```### Body Size TrendsThe annual stratified-abundance weighted body length and body weight within each region and for each functional group were also estimated using the numbers at length and the estimated biomass at length information. Data for body size trends was not truncated at 1g minimum body weight.```{r}#| label: body size data# Load the average body size datawithr::with_dir(rprojroot::find_root('_targets.R'), tar_load(mean_sizes_ss_groups)) # Grouped on year and regionregional_size <- mean_sizes_ss_groups %>%filter(`group ID`=="single years * region") %>%mutate(Year =as.numeric(Year),factor(survey_area, levels =c("GoM", "GB", "SNE", "MAB")))```### Size Spectrum AnalysisCommunity size spectra were estimated using abundance-at-length data from `r n_species` species. These species were selected based on the availability of published weight-at-length relationships (Wigley et al. 2003) and represented 98.98% of the total biomass caught in the survey. Published length-weight relationships were used to convert abundance at length data into their corresponding biomass at length (kg). These values were then used to get totals for stratified weight-at-length, in complement to the corrected abundances-at-length data which had been area stratified. These area-stratified biomass at length totals were then used for fitting each regional biomass size spectra.To fit the normalized biomass size spectra, stratified biomass at length data was binned into logartithmically equal spaced intervals (0.5 on a $log_{10}$ scale), summing bodymass across all species within each body size bin. To normalize the spectra, the atratified abundances within each bin was then divided by the bin-width to account for the increasing bin-widths, a consequence of the log scale. Normalized size spectra were fit for each year and for each region independently, and for each year across all strata, using ordinary least squares (ols) regressions for stratified abundance (normalized) by body-size bins.```{r}#| label: load the size spectrum indiceswithr::with_dir(rprojroot::find_root('_targets.R'), tar_load(size_spectrum_indices)) # Grab SS Groups we care aboutregion_indices <- size_spectrum_indices %>%filter(`group ID`=="single years * region") %>%mutate(yr =as.numeric(as.character(Year)),survey_area =factor(survey_area, levels =c("GoM", "GB", "SNE", "MAB")),sig_fit =ifelse(l10_sig_strat <0.05, "Significant", "Non-Significant"))```## Temperature DataGlobal Sea surface temperature data was obtained via NOAA's optimally interpolated SST analysis (OISSTv2), providing daily temperature values at a 0.25° latitude x 0.25° longitude resolution (Reynolds et al. 2007). A daily climatology for every 0.25° pixel in the global data set was created using average daily temperatures spanning the period of 1982-2011. Daily anomalies were then computed as the difference between observed temperatures and the daily climatological average. OISSTv2 data used in these analyses were provided by the NOAA PSL, Boulder, Colorado, USA from their website at https://psl.noaa.gov.Temperature data was regionally averaged to match the survey regions from the age-at-length data. SST anomalies were averaged by year for each region and over the entire sampling region to produce daily time series. These time series were then processed into annual timeseries of surface temperatures and anomalies. All region-averaging was done with area-weighting of the latitude/longitude grid cells to account for differences in cell-size in of the OISSTv2 data.```{r}#| label: load temperature data# Load the regional temperatures from {targets}withr::with_dir(rprojroot::find_root('_targets.R'), tar_load(regional_oisst))# Get regional averagestemp_regimes <- regional_oisst %>%filter(yr >1981) %>%mutate(regime =ifelse(sst_anom >0, "hot", "cold"),survey_area =ifelse(survey_area =="all", "Full Region", survey_area),survey_area =factor(survey_area, levels =c("Full Region", "GoM", "GB", "SNE", "MAB"))) ```## Spectra DriversThe impact of external factors on the changes in size spectra was correlated against several hypothesized driving forces related to both environmental regimes and anthropogenic disturbances. Potential environmental drivers include sea surface temperature anomalies, Gulf Stream Index (GSI), and zooplankton community indices from the continuous plankton recorder (CPR) dataset. Anthropogenic drivers include state and federal fisheries landings from the Greater Atlantic Regional Fisheries Office (GARFO), divided by reporting zones into aggregate regions to closely align with the survey areas we defined for the size spectra analyses.{fig-align="center",width = "420"}```{r}#| label: spectra-driver-data#| eval: true#### 1. GARFO Landings ##### Load the GARFO landings data:res_path <- gmRi::cs_path("res")# Landings of finfish* sheet 5landings <-read_xlsx(path =str_c(res_path, "GARFO_landings/KMills_landings by area 1964-2021_JUN 2022.xlsx"), sheet =5) %>%rename_all(tolower)# Shapefiles for the fisheries stat zonesstat_zones <-read_sf(str_c(res_path, "Shapefiles/Statistical_Areas/Statistical_Areas_2010_withNames.shp"))# Make a list of zones to roughly match the survey areas:fish_zones <-list("Gulf of Maine"=c(511:515, 464, 465),"Georges Bank"=c(521, 522, 525, 561, 562),"Southern New England"=c(611, 612, 613, 616, 526, 537, 538, 539),"Mid-Atlantic Bight"=c(614:615, 621, 622, 625, 626, 631, 632))# Trim out what we don't need and label themstat_zones <- stat_zones %>%mutate(survey_area =case_when( Id %in% fish_zones$"Gulf of Maine"~"Gulf of Maine", Id %in% fish_zones$"Georges Bank"~"Georges Bank", Id %in% fish_zones$"Southern New England"~"Southern New England", Id %in% fish_zones$"Mid-Atlantic Bight"~"Mid-Atlantic Bight")) %>%filter(survey_area %in%c("Georges Bank", "Gulf of Maine", "Southern New England", "Mid-Atlantic Bight"))# # Map it out? - takes forever from Chesapeake bay# garfo_map <- ggplot(stat_zones) +# geom_sf(aes(fill = survey_area), alpha = 0.8) +# geom_sf(data = us_poly) +# geom_sf(data = canada) +# coord_sf(xlim = c(-76.4, -64.4), ylim = c(35, 45.5), expand = F) +# scale_fill_gmri() +# theme_bw() +# map_theme(legend.position = "bottom",# legend.background = element_rect(fill = "white")) +# guides(fill = guide_legend(title = "", nrow = 2)) +# labs(title = "GARFO Region Assignments")# ggsave(garfo_map, filename = here::here("R/nmfs_size_spectra/images/garfo_regions.png"), bg = "white")# Add the labels into the landings data and remove what we don't need there:landings <- landings %>%mutate(survey_area =case_when(`stat area`%in% fish_zones$"Gulf of Maine"~"Gulf of Maine",`stat area`%in% fish_zones$"Georges Bank"~"Georges Bank",`stat area`%in% fish_zones$"Southern New England"~"Southern New England",`stat area`%in% fish_zones$"Mid-Atlantic Bight"~"Mid-Atlantic Bight")) %>%filter(survey_area %in%c("Georges Bank", "Gulf of Maine", "Southern New England", "Mid-Atlantic Bight")) %>%mutate(survey_area =factor(survey_area, levels =c("Gulf of Maine", "Georges Bank", "Southern New England", "Mid-Atlantic Bight" )))# Get Summarieslandings_summ <- landings %>%drop_na() %>%rename("weight_lb"=`landed lbs`,"live_lb"=`live lbs`) %>%drop_na() %>%group_by(year, survey_area) %>%summarise( across(.cols =c(value, weight_lb, live_lb), .fns =list(mean = mean, total = sum), .names ="{.fn}_{.col}"), .groups ="drop") # Scale the landings to create an index by arealandings_summ <- landings_summ %>%group_by(survey_area) %>%mutate(total_wt_z = base::scale(total_weight_lb)) %>%ungroup()#### 2. Climate Drivers ##### From ecodata:#### NAO & GSI & WCR# North Atlantic Oscillationnao <- ecodata::nao %>%mutate(year = Time,Time =as.Date(str_c(Time, "-01-01")))# Gulf Stream Indexgsi <- ecodata::gsi %>%mutate(Time =str_pad(as.character(Time), width =7, side ="right", pad ="0"),year =as.numeric(str_sub(Time, 1, 4)),month =str_sub(Time, -2, -1),Time =as.Date(str_c(year, month, "01", sep ="-")))# # Warm Core Rings# wcr<- ecodata::wcr %>%# mutate(# year = Time,# Time = as.Date(str_c(Time, "-01-01")))#### 3. CPR Indices ##### From CPR Analysis:# Uses 7 focal taxa, 2 calanus stagescpr_indices <-read_csv("~/Documents/Repositories/continuous_plankton_recorder/results_data/cpr_focal_pca_timeseries_period_1961-2017.csv")# Do some Reshaping for the Plotcpr_indices <- cpr_indices %>%select(c(year, 2:3)) %>%rename(`Principal Component 1`="First Mode",`Principal Component 2`="Second Mode") %>%pivot_longer(names_to ="pca_mode", values_to ="pca_loading", cols =c(2:3))# # Zooplankton densities from ECOMON?# # tidy the calanus stage abundance data# cal <- ecodata::calanus_stage %>% # rename(year = Time,# var = Var,# area = EPU) %>% # group_by(var, area) %>% # mutate(value = scale(Value)) %>% # ungroup() %>% # select(-Value)``````{r}#| label: all-drivers-table#### Build a Common form for all Drivers: ##### Format: year, area, var, value# table to join for swapping shorthand for long-hand namesarea_df <-data.frame(area =c("Scotian Shelf", "Gulf of Maine", "Georges Bank", "Southern New England", "Mid-Atlantic Bight", "All"),survey_area =c("SS", "GoM", "GB", "SNE", "MAB", "Full Region"))#--------------------# 1.Climate Features# Join the climate modes togetherclim_drivers <-bind_rows(list(#nao, # Only Goes to 2018, also not strongly correlated gsi#, #wcr # Too related to GSI )) %>%filter(EPU =="All")# Put climate drivers on annual scheduleclim_idx <- clim_drivers %>%group_by(year, area = EPU, var = Var) %>%summarise(value =mean(Value, na.rm = T),.groups ="drop")#--------------------# 2. GARFO landingslandings_idx <- landings_summ %>%group_by(year, area = survey_area) %>%summarise(value =mean(total_wt_z, na.rm = T),.groups ="drop") %>%mutate(var ="landings")#--------------------# 3. CPR Zooplanktoncpr_idx <- cpr_indices %>%mutate(area ="Gulf of Maine",var =str_c("cpr_pca_", str_sub(pca_mode, -1, -1))) %>%group_by(year, area, var) %>%summarise(value =mean(pca_loading),.groups ="drop")#--------------------# 4. Temperaturesst_idx <- temp_regimes %>%left_join(area_df, by ="survey_area") %>%select(year = yr, area, value = sst_anom) %>%mutate(var ="sst_anom")#--------------------# 5. Size Spectrum Slopes & Interceptsss_idx <- region_indices %>%select(year = Year, survey_area, spectra_slope = l10_slope_strat, spectra_int = l10_int_strat) %>%pivot_longer(cols =c(spectra_slope, spectra_int), names_to ="var", values_to ="value") %>%mutate(year =as.numeric(year)) %>%left_join(area_df, by ="survey_area") %>%select(-survey_area)#--------------------# # ECODATA calanus# Not strongly correlated, also a big mess to handle# Less likely to be important relative to the broader zooplankton landscape# # tidy the calanus stage abundance data# # Scale the abundances within each area# cal_idx <- ecodata::calanus_stage %>% # mutate(EPU = ifelse(EPU == "GOM", "GoM", EPU)) %>% # group_by(Var, EPU) %>% # mutate(value = as.numeric(scale(Value))) %>% # ungroup() %>% # select(-Value, -Units) %>% # setNames(c("year", "var", "survey_area", "value")) %>% # left_join(area_df, by = "survey_area") %>% # select(-survey_area)#--------------------# All Metrics Togetherall_drivers <-bind_rows(list( clim_idx, landings_idx, cpr_idx,#cal_idx, sst_idx, ss_idx ))``````{r}#| label: driver-matrix-correlations# 1. Split the Matrix into two time periodsdriver_periods <- all_drivers %>%mutate(period =ifelse(year <=1995, "exploitation", "recovery")) %>%bind_rows(mutate(all_drivers, period ="full")) %>%split(.$period)# Run Correlations for Each period and for the entire periodperiod_correlations <-map(driver_periods, function(drivers){# Step 1.# Do some reshaping to create a matrix for correlations: driver_matrix <- drivers %>%mutate(id =str_replace_all(str_c(area, "_", var), " ", "_")) %>%select(-c(area, var, period)) %>%pivot_wider(names_from = id, values_from = value) %>%#filter(if_any(everything(), is.na)) %>% tail() # Finding NA's in any columndrop_na() %>%column_to_rownames(var ="year")# Step 2. # Get the correlation matrix after scaling the data rho <-cor(scale(driver_matrix))# Step 3. # Make it a dataframe for GGplot correlation Matrix gg_rho <-as.data.frame(rho) %>%rownames_to_column(var ="xvar") %>%pivot_longer(cols =-xvar, names_to ="yvar", values_to ="r2")# Step 4. # Tidy it up, enhance the labels for plotting# label the drivers and the responses specifically so we can group them# in an orderly way gg_rho_tidy <- gg_rho %>%mutate(# Slope and intercept are the only size spectra featuresresponse_var =ifelse(str_detect(xvar, "slope|int"), T, F),# These are the drivers, use string detectdriver_var =ifelse(str_detect(yvar, "adt|c5|osc|pca|landings|index|anom"), T, F),area =case_when(str_detect(xvar, "Georges") ~"Georges Bank",str_detect(xvar, "Gulf") ~"Gulf of Maine",str_detect(xvar, "Southern") ~"Southern New England",str_detect(xvar, "Mid-Atlantic") ~"Mid-Atlantic Bight"),area =factor(area, levels =c("Gulf of Maine", "Georges Bank", "Southern New England", "Mid-Atlantic Bight")),driver_type =case_when(str_detect(yvar, "landings") ~"Fishing",str_detect(yvar, "adt|c5") ~"Calanus",str_detect(yvar, "pca|stream|anom|osci") ~"Environmental"),x_short =case_when(str_detect(xvar, "slope") ~"Spectra Slope",str_detect(xvar, "int") ~"Spectra Intercept"))# Do some filtering and string formatting: only_driver_response <- gg_rho_tidy %>%filter( response_var, driver_var) %>%mutate(yvar =str_replace_all(yvar, "_", " "),yvar =factor(yvar, levels =c("Gulf of Maine cpr pca 1","Gulf of Maine cpr pca 2","All gulf stream index","All north atlantic oscillation","All sst anom","Gulf of Maine sst anom","Georges Bank sst anom", "Southern New England sst anom","Mid-Atlantic Bight sst anom","Gulf of Maine landings","Georges Bank landings","Southern New England landings","Mid-Atlantic Bight landings" ))) # Spit out all the pieces in an organized list correlation_details <-list("drivers_matrix"= driver_matrix,"rho"= rho,"ggrho"= gg_rho,"ggrho_tidy"= gg_rho_tidy,"only_driver_response"= only_driver_response )})```# Results### Abundance DistributionAbundance across all body sizes remained relatively stable from the 1970's before rising in the northern regions around 1990 beginning in the Gulf of Maine. Around this time abundances increased through the mid 2010's. Further south in Georges Bank, abundances remained flat until the 2010's, when overall abundance roughly tripled, only to fall back to previous amounts by the end of the century. Southern New England saw a less dramatic rise and fall that began just before 2010, again falling back to earlier levels by the end of the century. The Mid Atlantic Bight had relatively consistent abundances throughout, with no major periods of abundance growth or decline, but with larger inter-annual variability.```{r}#| label: abundance distributions#| fig.width: 8#| fig-height: 8# panel plot the biomass by body sizefgroup_area$weight_bins <- fgroup_area$weight_bins %>%mutate(strat_abund_mill = wtbin_strat_abund /1e6) # Just abundance, not by groupsfgroup_area$weight_bins %>%group_by(Year, survey_area, spec_class) %>%summarise(abund_millions =sum(strat_abund_mill, na.rm = T),.groups ="drop") %>%ggplot(aes(Year, abund_millions, fill = spec_class)) +geom_area() +scale_fill_gmri() +scale_x_continuous(expand =expansion(add =c(0,0))) +facet_wrap(~survey_area, ncol =1) +labs(y ="Abundance (millions)", title ="Estimated Total Abundance from\nArea-Stratified Catch Rates",x ="",fill ="Functional Group") +theme(legend.position ="bottom",panel.background =element_rect(colour ="black", size=2, fill =NA))```Abundance gains observed in Georges Bank and Gulf of Maine were primarily from groundfish species, with additional growth from demersal species in the Gulf of Maine. Increases in abundance across all areas was mostly confined to individuals weighing less than .5kg. With some years driven in large-part by exceptional year-classes in a handful of species like haddock in Georges Bank. The observed abundance volatility in Southern New England and the Mid-Atlantic Bight conversely was largely the result of changes in abundance in pelagic species, whose abundance varied by several times the magnitude that of the other functional groups.```{r}#| label: abundance-by-group#| fig.height: 8#| fig.width: 8#| eval: true# Plot abundance by region and Groupfgroup_area$weight_bins %>%ggplot(aes(Year, strat_abund_mill, fill = weight_bin)) +geom_area() +facet_grid(survey_area~spec_class) +scale_fill_gmri() +scale_y_continuous(labels =comma_format()) +labs(y ="Stratified Abundance (millions)", title ="Abundance Allocation by Weight",x ="", fill ="Individual Weight (kg)") +theme(legend.position ="bottom",axis.text.x =element_text(angle =45, hjust =1))```### Biomass DistributionOverall biomass was highest in the two northern regions, the Gulf of Maine and Georges Bank. Roughly half of the biomass sampled in these regions can be attributed to groundfish/demersal species, with the second largest contributions coming from elasmobranchs. Groundfish biomass, larger individuals \>2kg in particular, declined during the 70's and 80's in these regions, never truly recovering. Beginning in the 2000's there were signs that groundfish abundances were increasing as evidenced by increasing numbers of smaller individuals, however in both regions this trend appears to have reversed by the mid 2010's. Elasmobranch biomass increased steadily throughout the survey time period across all regions, with the exception of southern New England. This area showed large 5-10 year swings in biomass, but no clear long-term trend. Larger elasmobranch were rare in all regions except for a period spanning the late 70's through the early 90's isolated to Georges Bank. Demersal species biomass was highest in the Gulf of Maine, dwarfing their contributions in other regions. Their biomass declined in the 70's, was flat until the late 90's, remaining relatively high until declining in the late 2010's. Pelagic species biomass was low in all regions, and is unlikely to be representative of true biomass trends due to gear selectivity.```{r}#| label: bodymass distributions#| fig.width: 8#| fig-height: 8# panel plot the biomass by body sizefgroup_area$weight_bins %>%mutate(strat_lwbio_mill = wtbin_strat_lw_bio /1e6) %>%ggplot(aes(Year, strat_lwbio_mill, fill = weight_bin)) +geom_area() +facet_grid(survey_area~spec_class) +scale_fill_gmri() +scale_y_continuous(labels =comma_format()) +labs(y ="Stratified Total Biomass (million kg)", title ="Biomass Allocation by Weight",x ="", fill ="Individual Weight (kg)") +theme(legend.position ="bottom",axis.text.x =element_text(angle =45, hjust =1))```### Regional Variation in Species CompositionThere was a distinct difference between Northern and Southern regions in the way biomass was distributed among the different functional groups. The primary contributors to overall biomass in the southern regions (southern New England & mid-Atlantic bight) was the elasmobranch community. While the northern regions (Gulf of Maine & Georges Bank) each had similar quantities of elasmobranch biomasses, there was also a comparable contribution of groundfish and in the Gulf of Maine there was a major component of demersal species as well.### Body Size TrendsThe average fish size in the Gulf of Maine (length and weight) declined the greatest of all regions over our study period. The average individual length was greatest in the 1970's in the 35-40cm range, falling to 28-33cm over the last decade. Body-weight fell dramatically in the 1980's, from around .75kg in the 1970's to .25-.30kg, roughly a third of what it had been. Georges Bank body sizes also declined during the study period, but less dramatically. Both of these Northern regions had brief period in the late 2000's where average length and weight rose, before falling again in the 2010's. The MAB region was the only region to see a long-term increase in both length and weight during the study period. SNE saw no long-term change in length, and a minor decline in average body-weight.```{r}#| label: average body size trends#| fig.height: 8#| fig.weight: 8# Re-factorregional_size <- regional_size %>%mutate(survey_area =factor(survey_area, levels =c("GoM", "GB", "MAB", "SNE")),area_copy = survey_area)# Length plotavg_len_p <- regional_size %>%ggplot(aes(Year, mean_len_cm)) +geom_line(data =select(regional_size, -survey_area),aes(group = area_copy),alpha =0.2, size =0.5) +geom_line(aes(group = survey_area), size =1) +#theme_minimal() +facet_wrap(~survey_area, ncol =1) +labs(title ="Average Length",y ="Length (cm)")# Weight plotavg_wt_p <- regional_size %>%ggplot(aes(Year, mean_wt_kg)) +geom_line(data =select(regional_size, -survey_area),aes(group = area_copy),alpha =0.2, size =0.5) +geom_line(aes(group = survey_area), size =1) +# theme_minimal() +facet_wrap(~survey_area, ncol =1) +labs(title ="Average Weight",y ="Weight (kg)")# Plot side by sideavg_len_p | avg_wt_p```### Regional Size SpectraSize spectrum slopes were least steep in the two Northern regions.- Steepening of northern slopes- lack of bounce-back- all slopes similar steepness```{r}#| label: size spectra results#| fig.height: 8#| fig.weight: 8# Make a copy so we can gray out the linesregion_indices <-mutate(region_indices, area_copy = survey_area)# Plot the Regional Slopesslope_timeline <- region_indices %>%ggplot(aes(yr, l10_slope_strat, group = survey_area)) +geom_line(data =select(region_indices, -survey_area),aes(group = area_copy),alpha =0.2, size =0.5) +geom_line(aes(group = survey_area), size =1) +scale_color_gmri() +facet_wrap(~survey_area, ncol =1) +# theme_minimal() +labs(x ="Year", y ="Slope", title ="Normalized Biomass Spectra") # Plot the Regional Interceptsint_timeline <-ggplot(region_indices, aes(yr, l10_int_strat, group = survey_area)) +geom_line(data =select(region_indices, -survey_area),aes(group = area_copy),alpha =0.2, size =0.5) +geom_line(aes(group = survey_area), size =1) +facet_wrap(~survey_area, ncol =1) +# theme_minimal() +labs(x ="Year", y ="Intercept") # Assembleslope_timeline | int_timeline ```## Size Spectra Drivers### Driver Correlations ::: panel-tabset#### Exploitation Period (1970 - 1995)```{r}#| label: ggcorr-drivers-exploitation#| fig-height: 6#| fig-width: 8# Plot the convoluted thing:period_correlations$exploitation$only_driver_response %>%filter(driver_type !="Calanus") %>%ggplot(aes(yvar, x_short, fill = r2)) +geom_tile() +facet_grid(area~driver_type, scales ="free_x", space ="free") +scale_fill_distiller(palette ="RdBu", limits =c(-1, 1), breaks =c(-1,0,1)) +coord_cartesian(clip ="off") +theme(axis.text.x =element_text(angle =45, hjust =1, vjust =1),strip.text.y =element_text(angle =0),legend.position =c(1.205, -.25),legend.title =element_text(vjust =0.75)) +guides(fill =guide_colorbar(title.position ="top", title.hjust =0.5)) +labs(x ="Hypothesized Driver", y ="Bodymass Spectra Coefficient",fill ="Correlation Coefficient",title ="Correlation of Size Spectra to Hypothesized Drivers | Exploitation Era",subtitle ="Correlation coefficients shown display same-year correlations.")```#### Recovery Period (1996-2019)```{r}#| label: ggcorr-drivers-recovery#| fig-height: 6#| fig-width: 8# Plot the convoluted thing:period_correlations$recovery$only_driver_response %>%filter(driver_type !="Calanus") %>%ggplot(aes(yvar, x_short, fill = r2)) +geom_tile() +facet_grid(area~driver_type, scales ="free_x", space ="free") +scale_fill_distiller(palette ="RdBu", limits =c(-1, 1), breaks =c(-1,0,1)) +coord_cartesian(clip ="off") +theme(axis.text.x =element_text(angle =45, hjust =1, vjust =1),strip.text.y =element_text(angle =0),legend.position =c(1.205, -.25),legend.title =element_text(vjust =0.75)) +guides(fill =guide_colorbar(title.position ="top", title.hjust =0.5)) +labs(x ="Hypothesized Driver", y ="Bodymass Spectra Coefficient",fill ="Correlation Coefficient",title ="Correlation of Size Spectra to Hypothesized Drivers | Recovery Era",subtitle ="Correlation coefficients shown display same-year correlations.")```#### Full Time Series```{r}#| label: ggcorr-drivers-full#| fig-height: 6#| fig-width: 8# Plot the convoluted thing:period_correlations$full$only_driver_response %>%filter(driver_type !="Calanus") %>%ggplot(aes(yvar, x_short, fill = r2)) +geom_tile() +facet_grid(area~driver_type, scales ="free_x", space ="free") +scale_fill_distiller(palette ="RdBu", limits =c(-1, 1), breaks =c(-1,0,1)) +coord_cartesian(clip ="off") +theme(axis.text.x =element_text(angle =45, hjust =1, vjust =1),strip.text.y =element_text(angle =0),legend.position =c(1.205, -.25),legend.title =element_text(vjust =0.75)) +guides(fill =guide_colorbar(title.position ="top", title.hjust =0.5)) +labs(x ="Hypothesized Driver", y ="Bodymass Spectra Coefficient",fill ="Correlation Coefficient",title ="Correlation of Size Spectra to Hypothesized Drivers | All Years",subtitle ="Correlation coefficients shown display same-year correlations.")```:::# DiscussionThe data we rely on in this analysis was collected as part of a survey program which began out of concern that fisheries were already being over-harvested. Estimates by scientists at that time suggested that by the 1970's total biomass of Georges Bank had been halved and elasmobranchs had begun to replace the over-exploited gadoids [@fogarty1998]. The implication of such a large disturbance that pre-dates our time series is that the steepening of size spectrum slope we observed in this area and the adjacent Gulf of Maine are the tail-ends of a longer and more severe decline. While metrics of overall fishing pressure is hard to align exactly with trawl survey coverage, historic and anecdotal evidence show that groundfish fishing pressures are a fraction of their historic pressure was in the 1960's and 1970's. This begs the question of why larger adult numbers never began to recover in these regions. Looking at abundance and biomass information from the survey there was evidence of strong recruitment among smaller individuals \< 1kg, but there has since not been any sizable population of fishes larger than 1kg outside of elasmobranchs. Work by [@pershing2015] suggests that part of the failure in recovery was due to an inability to account for temperature change in fisheries management. At this point in time the regional temperatures had just begun to reflect a regime shift, and could have been considered at that time an acute stressor. Since then the region has experienced nearly a decade of sustained above-average temperatures, and there are signs that the success seen in recruitment and survival of even the smaller size classes is declining.### Potential Drivers:::: panel-tabset#### Surface Temperature Anomalies```{r}#| label: temperature-index#| fig.height: 8# Plot the timelinestemp_regimes %>%mutate(anom_direction =ifelse(sst_anom >0, "Positive", "Negative")) %>%ggplot( aes(yr, sst_anom)) +geom_col(aes(fill = anom_direction), size =0.75, alpha =0.8) +geom_hline(yintercept =0, size =0.5, lty =1) +scale_x_continuous(expand =expansion(add =c(0.25,0.25))) +scale_y_continuous(labels =number_format(suffix =" \u00b0C")) +scale_fill_gmri() +facet_wrap(~survey_area, ncol =1) +theme(legend.title =element_blank(),legend.background =element_rect(fill ="transparent"),legend.key =element_rect(fill ="transparent", color ="transparent")) +labs(title ="NW-Atlantic Regional SST Anomalies",x ="", y ="Surface Temperature Anomaly",caption ="Anomalies calculated using 1982-2011 reference period.") +guides(fill ="none",label ="none",color =guide_legend(keyheight =unit(0.5, "cm")))```#### GARFO Landings```{r}#| label: landings-index#| fig.height: 8#### Landings ####landings_summ %>%mutate(anom_direction =ifelse(total_wt_z >0, "Positive", "Negative")) %>%ggplot(aes(year, total_wt_z, group = survey_area)) +geom_col(aes(fill = anom_direction), size =0.75, alpha =0.8) +geom_hline(yintercept =0, size =0.5, lty =1) +scale_fill_gmri() +facet_wrap(~survey_area, ncol =1) +scale_x_continuous(breaks =seq(1950, 2030, by =10)) +labs(x ="Year", y ="Finfish Landings Index (z)",title ="GARFO Finfish Landings") +guides(fill ="none")```#### Climate Modes```{r}#| label: climate-modes#### Climate Drivers ####clim_drivers %>%group_by(year = lubridate::year(Time), Var, EPU) %>%summarise(Value =mean(Value, na.rm = T)) %>%mutate(anom_direction =ifelse(Value >0, "Positive", "Negative")) %>%ggplot(aes(year, Value, group = EPU)) +geom_col(aes(fill = anom_direction), size =0.75, alpha =0.8) +geom_hline(yintercept =0, size =0.5, lty =1) +scale_fill_gmri() +facet_wrap(~Var, ncol =1) +scale_y_continuous(labels = scales::comma_format()) +scale_x_continuous(limits =c(1960, 2020), breaks =seq(1950, 2030, by =10)) +labs(x ="Year", y ="Total Finfish Landings (lb.)",title ="Environmental Drivers") +guides(fill ="none")```#### CPR PCA```{r}#| label: cpr-modes#### CPR Community ##### CPR Plotcpr_indices %>%mutate(anom_direction =ifelse(pca_loading >0, "Positive", "Negative")) %>%ggplot(aes(year, pca_loading, group = pca_mode)) +geom_col(aes(fill = anom_direction), size =0.75, alpha =0.8) +geom_hline(yintercept =0, size =0.5, lty =1) +scale_fill_gmri() +facet_wrap(~pca_mode, ncol =1) +scale_x_continuous(breaks =seq(1950, 2030, by =10)) +labs(x ="Year", y ="PCA Loading", title ="Gulf of Maine CPR PCA") +guides(fill ="none")```:::### Supplemental Materials#### Species Functional Groups#### GARFO Landings Summaries```{r}# Add the labels into the landings data and remove what we don't need there:landings %>%mutate(disturbance_era =ifelse(year <=1995, "1964 - 1995", "1996 - 2021"),sppname =str_to_title(sppname)) %>%group_by(survey_area, disturbance_era, sppname) %>%summarise(avg_landings_lb =mean(`landed lbs`, na.rm = T),across(.cols =c(landings_lb =`landed lbs`, value = value), .fns = sum, .names ="total_{.col}")) %>%slice_max(order_by = total_landings_lb, n =2) %>%#ungroup() %>% gt() %>%fmt_number(columns =c(avg_landings_lb, total_landings_lb, total_value),use_seps = T, sep_mark =",",suffixing = T) %>%cols_label(sppname ="Harvest Region & Time Period",avg_landings_lb ="Avg. Annual Landings (lb.)",total_landings_lb ="Total Landings (lb.)",total_value ="Total Value ($)") %>% gt::tab_header(title ="Top Landings by Weight")```# References
{fig-align=“center”,width = “420”}
